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Climatic Change

, Volume 91, Issue 3–4, pp 451–476 | Cite as

Uncertainty in resilience to climate change in India and Indian states

  • Elizabeth L. Malone
  • Antoinette L. Brenkert
Article

Abstract

This study builds on an earlier analysis of resilience of India and Indian states to climate change. The previous study (Brenkert and Malone, Clim Change 72:57–102, 2005) assessed current resilience; this research uses the Vulnerability–Resilience Indicators Model (VRIM) to project resilience to 2095 and to perform an uncertainty analysis on the deterministic results. Projections utilized two SRES-based scenarios, one with fast-and-high growth, one with delayed growth. A detailed comparison of two states, the Punjab and Orissa, points to the kinds of insights that can be obtained using the VRIM. The scenarios differ most significantly in the timing of the uncertainty in economic prosperity (represented by GDP per capita) as a major factor in explaining the uncertainty in the resilience index. In the fast-and-high growth scenario the states differ most markedly regarding the role of ecosystem sensitivity, land use and water availability. The uncertainty analysis shows, for example, that resilience in the Punjab might be enhanced, especially in the delayed growth scenario, if early attention is paid to the impact of ecosystems sensitivity on environmental well-being of the state. By the same token, later in the century land-use pressures might be avoided if land is managed through intensification rather than extensification of agricultural land. Thus, this methodology illustrates how a policy maker can be informed about where to focus attention on specific issues, by understanding the potential changes at a specific location and time—and, thus, what might yield desired outcomes. Model results can point to further analyses of the potential for resilience-building.

Keywords

Adaptive Capacity Vulnerability Assessment Economic Prosperity Integrate Assessment Model Environmental Capacity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Battelle Memorial Institute 2008

Authors and Affiliations

  1. 1.Joint Global Change Research InstituteCollege ParkUSA

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